Mahesh Venkata Krishna

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The concept behind the next generation wireless networks is the coexistence of different radio access networks aimed for integration of different set of technologies. In turn these networks will have various kinds of parameters like Jitter, Delay, Bandwidth, Area of Coverage and Power Consumption etc., which in turn impose new challenges on mobility(More)
Temporal segmentation of videos into meaningful image sequences containing some particular activities is an interesting problem in computer vision. We present a novel algorithm to achieve this semantic video segmentation. The segmentation task is accomplished through event detection in a frame-by-frame processing setup. We propose using one-class(More)
Recent approaches in traffic and crowd scene analysis make extensive use of non-parametric hierarchical Bayesian models for intelligent clustering of features into activities. Although this has yielded impressive results, it requires the use of time consuming Bayesian inference during both training and classification. Therefore, we seek to limit Bayesian(More)
One of the most important and challenging tasks in bio-medical image analysis is the localization, identification, and discrimination of salient objects or structures. While to date human experts are performing these tasks manually at the expense of time and reliability, methods for automation of these processes are evidently called for. This paper outlines(More)
Segmenting videos into meaningful image sequences of some particular activities is an interesting problem in computer vision. In this paper, a novel algorithm is presented to achieve this semantic video segmentation. The goal is to make the system work unsupervised and generic in terms of application scenarios. The segmentation task is accomplished through(More)
This paper propose a novel framework for unsupervised detection of object interactions in video sequences based on dynamic features. The goal of our system is to process videos in an unsupervised manner using Hierarchical Bayesian Topic Models, specifically the Hierarchical Dirichlet Processes (HDP). We investigate how low-level features such as optical(More)
We propose a hierarchical Bayesian model-the wordless Hierarchical Dirichlet Processes-Hidden Markov Model (wHDP-HMM), to tackle the problem of unsupervised cell phenotype clustering during the mitosis stages. Our model combines the unsupervised clustering capabilities of the HDP model with the temporal modeling aspect of the HMM. Furthermore, to model cell(More)
In various real-world applications of distributed and multi-view vision systems, the ability to learn unseen actions in an online fashion is paramount, as most of the actions are not known or sufficient training data is not available at design time. We propose a novel approach which combines the unsupervised learning capabilities of Hierarchical Dirichlet(More)
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